Specified identifiers such as logos are generally recognized according to various physical detection mechanisms. Specified identifiers are generally used to indicate a certain feature of an object such as a credit card. Identifiers can be used as a reference for recognizing objects (e.g., identifying an account associated with the object or owner of the object, or identifying a type of object). Objects that include specified identifiers generally have specified identifiers that have distinct appearances or notable spatial locations, so that the objects or specified identifiers are more easily identified. The specified identifiers can be implemented as an identifier object on the object such as the card. For bank cards, identifier objects generally include the graphic identifiers and text identifiers of the card issuer.
The recognition of identifier objects can serve as an important basis for distinguishing the authenticity of the bank card. For example, the identifier objects can be verified to determine whether the object comprising the specified identifiers is genuine or valid.
Identifier objects are generally associated with defined size specifications and relative location identifiers. For example, authentic bank cards are generally printed using standard identifier objects. However, because the workmanship of counterfeit bank cards is poor and because counterfeiting bank cards (or other objects carrying specified identifiers that can be implemented in the form of identifier objects) is performed at a lower cost, printing controls associated with the printing of authentic bank cards are generally not stringent. Accordingly, recognition of the identifier objects can be used to obtain the dimensions and relative positioning of the identifier objects. Comparison of the obtained dimensions and relative positioning of the identifier objects to a standard bank card can provide an effective reference for distinguishing authenticity of a bank card.
The recognition of identifier objects can provide reference identifiers for bank card detection and correction.
Bank cards generally have associated defined uniform style rules for identifier objects. For example, bank cards can have defined edge identifiers. The defined edge identifiers can provide a clear indication that the card or other object is a bank card. An image of the card to be analyzed can be captured. An edge can be obtained based on recognition of the defined edge identifier. The obtained edge can be used to obtain the currently captured image via an affine transformation based on the obtained edge, thus enabling correction of the image to benefit subsequent precision detection and positioning of the bank card. A conventional bank card according to some related art may not be associated with unified standard. However, a conventional bank card can comprise markers that generally have clear dimensions and relative position. Accordingly, edge information of such bank cards is typically relatively clear. An identifier can be obtained via an affine transformation using an affine transformation matrix. According to some related art, the identifier would have the specifications of the marker. Because bank cards and markers according to some related art are coplanar, the same affine transformation matrix can be used to obtain bank card numbers affine transformation matrix, to obtain a corrected image, and to help with detection and localization.
The recognition of identifier objects can enable service providers to engage in accurate advertising push services based on specified identifier objects.
Identifier objects often indicate specified identifiers of a bank card, for example, the card issuer and the scope of use. Accordingly, the recognition of identifier objects enables businesses to engage in accurate advertising push services targeting the bank card, and increases service quality. In addition, in the event that an identifier of the identifier object is retrieved, and is combined with augmented reality technology, multimedia advertising displays can be implemented.
In a given image or video segment, object detection can include finding the location of a specified identifier or an object of interest in the image or video. For example, according to specific use scenarios, the final output of object detection systems is generally an enclosing rectangular frame or the precise contour of the object.
According to some related art, two categories of object detection are generally used. The two categories of object detection include object detection using a sliding window and object detection using a generalized Hough transform. The sliding window object detection method includes the use of a trained model to perform a sliding scan of multiple dimensions and multiple visual angles of an input image, to find the enclosing window of a target object by determining the maximum corresponding positions.
According to the sliding window method implemented by related art for object detection, when data is being analyzed and scanned, the computation volume is significant, and calculation complexity is also relatively high, which result in longer detection times that are better suited for more complex object structures. Further, the sliding window method is generally not suitable for recognizing an identifier that has a relatively fixed structure.